Chemical Cartography via High-Throughput Experimentation: Predictive Models, Catalyst Development, and New Synthetic Methodology

通过高通量实验进行化学制图：预测模型、催化剂开发和新的合成方法

• 批准号: RGPIN-2019-04985
• 负责人: Leitch, David
• 金额: $ 2.99万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737991
• 关键词: Chemical; Cartography; via; Throughput; Experimentation

Organic synthesis is among the most impactful scientific developments in history, dramatically improving quality of life via breakthroughs in medicine, agriculture, and materials. Despite these advances and more than a century of research, in most labs the practice of organic synthesis is remarkably unchanged from how it was done in the early 1900s. Individual chemical reactions are optimized through an iterative and often trial-and-error approach using single experiments carried out in flasks. While this has worked well in the past, we are now at a point where continued progress in the field requires new, more efficient techniques and tools to enable a deeper understanding of chemical reactivity. The theme of the Leitch group program is "the exploration of uncharted chemical space." This means finding new points on the map (novel chemical structures), and studying the paths between these points (chemical reactivity). Specifically, we will address a centrally important but still unsolved problem in organic chemistry: how can one predict chemical reactivity in a quantitative manner, and use these predictions to develop new and more efficient chemical syntheses? My group will tackle this problem by combining fundamental physical chemistry principles with modern high-throughput experimental methods and data analytics. We will use this approach to generate quantitative mechanistic models - i.e. maps of chemical reactivity - for key chemical reactions currently used for pharmaceutical synthesis, and to develop scalable syntheses of novel three-dimensional carbon frameworks that are at the forefront of modern drug discovery research. Critical to this endeavour is the simultaneous measurement of hundreds-to-thousands of chemical reaction rates and activation energies using high-throughput experimentation. Combining these values with computed molecular parameters for each chemical species will generate large, reliable, and consistent data sets. The size and mechanistic foundation of these data sets will be a distinct advantage in building meaningful quantitative models via algorithm-driven statistical analysis. These models will allow us to predict the outcome of a chemical reaction under a variety of hypothetical conditions, leading to a deeper and more holistic understanding of the factors that control chemical reactivity. The potential impact of this research in both academic and industrial contexts is substantial. The ability to predict the outcome of a given reaction will save countless person-hours in the pursuit of new therapeutics, agrochemicals, and advanced materials. Being able to quantitatively map how chemical structure affects reactivity will enable the discovery of new and more efficient syntheses in a rational manner. Finally, our reactivity maps will be powerful data sets on which to build predictive artificial intelligence systems for chemical synthesis design; this facet is one of the ultimate goals of this program.

有机合成是历史上最具影响力的科学医学发展之一，通过农业和材料方面的突破极大地提高了生活质量，尽管有这些进步和一个多世纪的研究，但在大多数实验室中，有机合成的实践相对没有变化。 1900 年代初，各个化学反应是通过在烧瓶中进行的单个实验进行迭代和反复试验的方法来优化的，虽然这种方法在过去效果很好，但现在我们正处于继续进行的阶段。该领域的进展需要新的、更有效的技术和工具来更深入地了解化学反应性，Leitch 小组计划的主题是“探索未知的化学空间”，这意味着在地图上寻找新的点（新的化学结构）并进行研究。具体来说，我们将解决有机化学中一个至关重要但尚未解决的问题：如何以某种方式定量预测化学反应性，并利用这些预测来开发新的、更有效的化学合成？我的小组将通过将基本物理化学原理与现代高通量实验方法和数据分析相结合来解决这个问题，我们将使用这种方法为目前用于药物合成的关键化学反应生成定量机制模型（即化学反应性图），并开发。新型三维碳框架的可扩展合成是现代药物发现研究的前沿，这一努力的关键是使用高通量同时测量数百到数千个化学反应速率和活化能。将这些值与每种化学物质的计算分子参数相结合将生成大量、可靠且一致的数据集，这些数据集的大小和机制基础将在通过算法驱动的统计构建有意义的定量模型方面具有明显的优势。这些模型将使我们能够预测各种假设条件下化学反应的结果，从而更深入、更全面地了解控制化学反应性的因素以及这项研究在学术和工业领域的潜在影响。是重要的。预测结果的能力特定的反应将在追求新疗法、农用化学品和先进材料的过程中节省无数的工时。反应图将成为强大的数据集，可在其上构建用于化学合成设计的预测人工智能系统；这方面是该计划的最终目标之一。